Optimal Classification under Performative Distribution Shift

TL;DR

This paper introduces a new framework for performative classification that models distribution shifts as push-forward measures, enabling scalable learning and robust risk minimization under unknown shift operators.

Contribution

It proposes a novel performative risk framework using push-forward measures, with scalable gradient estimation and convexity results for classification under distribution shifts.

Findings

Convexity of performative risk under new assumptions

Connection established between performative and adversarial robustness

Validated approach on synthetic and real datasets

Abstract

Performative learning addresses the increasingly pervasive situations in which algorithmic decisions may induce changes in the data distribution as a consequence of their public deployment. We propose a novel view in which these performative effects are modelled as push-forward measures. This general framework encompasses existing models and enables novel performative gradient estimation methods, leading to more efficient and scalable learning strategies. For distribution shifts, unlike previous models which require full specification of the data distribution, we only assume knowledge of the shift operator that represents the performative changes. This approach can also be integrated into various change-of-variablebased models, such as VAEs or normalizing flows. Focusing on classification with a linear-in-parameters performative effect, we prove the convexity of the performative risk under a new set of assumptions. Notably, we do not limit the strength of performative effects but rather their direction, requiring only that classification becomes harder when deploying more accurate models. In this case, we also establish a connection with adversarially robust classification by reformulating the minimization of the performative risk as a min-max variational problem. Finally, we illustrate our approach on synthetic and real datasets.